Episodes
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In the last episode of Season 1, Sara and Becca talk with Jesse Vincent about the shift from writing code to managing AI agents.
They break down how tools like Superpowers turn development into a system of planning, testing, and review, where subagents implement work and other agents validate it. The conversation explores what happens when coding becomes less about syntax and more about taste, judgment, and outcomes.
They also get into the reality of open source in the AI era, from “slop PRs” to agents impersonating maintainers, and why specs and tests may now matter more than the code itself. Along the way, they unpack how AI behaves like a team of eager but chaotic interns, and what it means to manage them effectively.
If you’ve ever felt like coding is changing faster than you can keep up, this episode explains what comes next.
Links mentioned:
⚡ Superpowers (Jesse’s tool) https://github.com/obra/superpowers 🛠️
🧠 Claude Code (agentic dev tooling) https://docs.anthropic.com/en/docs/claude-code 🤖
📊 Influence (book on persuasion principles) https://www.influenceatwork.com/book/ 📘
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Most people are using AI, but very few actually understand how to use it well.
In this episode, Sara and Becca talk with Navarrow Wright about what is really happening with AI adoption and why the biggest barrier is mindset, not technology. They break down why prompting matters, why AI is not one-size-fits-all, and how access to these tools is more open than any previous tech shift.
They also explore the real opportunity: using AI as a thought partner. From tools like NotebookLM to building without traditional technical skills, this episode shows what becomes possible when people actually understand how to use these systems.
If you feel like everyone else “gets AI” and you do not, this is a great place to start.
Links mentioned:
📓 NotebookLM📚 Gemini AI course🧠 Prompting guide (OpenAI)💻 Navarrow: IG, YT
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Missing episodes?
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We’re joined by Louise Macfadyen, author of Designing AI Interfaces, to talk about how we actually design for AI, and why most companies are getting it wrong.
We unpack “algorithmic transference” (why one bad AI experience makes you distrust all of them), the limits of the chat interface, and whether the text box is here to stay. Louise walks us through the history from command line → Google search → LLMs, and why designing for ambiguity is still one of the hardest problems in product.
We also get into generative UI (and why it might be a trap), how large companies should actually integrate AI without breaking user trust, and why understanding user intent matters more than ever. Plus: vibe coding, personal AI “jigs,” and the tension between personalization and privacy.
If you build products, or just use AI, this will change how you think about interfaces.
📘 Designing AI Interfaces · 🧠 Algorithmic Transference · 🛠️ Claude Code
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This week, Becca turns her birthday into a Twister masterclass by watching the movie with an AI “director’s commentary” running in real time (including some behind-the-scenes facts that sound… borderline illegal). Then Sara drops a mildly unhinged hot take: we might have already hit AGI—and we’ll never know it, because we don’t even agree on what “counts.”
From there, we’re joined by Hayden Helm, CEO and founder of Helivan, to talk about the next wave of agentic AI: personal bots that don’t just answer questions, but act—with tools, permissions, and the ability to change over time.
We get into:
Why agent behavior drift is the real risk (even when the bot still sounds “nice”)The rising need for agent observability: detecting change, quantifying it, and rolling back when things go sidewaysWhat happens when agents learn from the environment (and other agents), not just from youHayden’s work on “likeness” and opinion geometry—making messy human-ish behavior measurableWhy manual inspection won’t scale in a world of millions of autonomous interactionsIt’s a conversation about trust, safety, and the new security surface area we’re creating—one helpful assistant at a time.
🛠 Agent Infrastructure
OpenClawHelivan🔐 Agent Payments
Coinbase x402 Protocol / Agentic Wallets🧨 Agent Behavior Incident
“An AI Agent Published a Hit Piece on Me”
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Juliet Shen (Roost, formerly Snap, Grindr, Google) joins us to break down trust & safety — aka “anything bad that happens on the internet” — and why AI is changing the game for both attackers and defenders.
We talk safety-by-design (how to bake guardrails into product), the real human cost of content moderation, and where AI can actually help without pretending it solves everything. Juliet explains how agentic AI scales old harms like romance scams from a handful of conversations to thousands at once — and why open-source safety infrastructure matters when bad actors share tactics faster than platforms do.
We also dig into what Roost is building: Osprey (an investigation + rules engine) and Coop (a flexible review tool), plus the building blocks smaller teams need to ship products responsibly before the “weird edge cases” arrive.
If you build products, this one’s a must.
🛡️ Roost (GitHub + tools directory)
👥 Trust & Safety Professional Association (TSPA)
🧩 Prosocial Design Network
📚 Behind the Screen (Sarah T. Roberts — content moderation + human cost)
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We’re joined by Meghan Heintz, founding engineer at Herd Labs, to break down where crypto and AI agents actually work. We talk prediction markets, smart contracts, wallets, rug pulls, and how AI can finally explain what happened on-chain.
Then we zoom out to the human side: benchmarking and evals for agents, misinformation, the Mom Test, and what it feels like to manage coding agents instead of junior engineers.
Links mentioned: Polymarket · Dune Analytics · Herd Labs · The Mom Test
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Becca just got back from NeurIPS, the academic AI conference that feels like an adult science fair. We dig into research on training large AI models across cheap GPUs and slow internet connections—and why that could dramatically lower the barrier to building AI.
Then we’re joined by Harper Reed, CEO of 2389, for a wide-ranging conversation about code generation, coaching-based engineering teams, and why “production code” might have always been a myth. We talk vibe coding (begrudgingly), the shifting role of software engineers, taste vs. technical skill, and what happens when you can build almost anything in a week.
Smart, funny, and a little unsettling—Chaos Agents at full volume.
🎓 Academic AI & research cultureNeurIPS (Conference on Neural Information Processing Systems)NeurIPS 2024 Accepted Papers
🧠 Distributed training, GPUs & efficiencyNVIDIA H100 Tensor Core GPU (referenced GPU class)Pluralis Research (distributed training across low-bandwidth networks)
⚙️ Core AI concepts mentionedGPU vs CPU explained (parallel vs sequential compute)Data Parallelism vs Model Parallelism (training overview)
🧑💻 Code generation & developer toolsClaude Code (Anthropic code-gen tooling)Cursor (AI-first code editor, discussed implicitly)
🛠️ Agent workflows & infrastructureMatrix (open-source, decentralized chat protocol)Model Context Protocol (MCP) overview
🧩 Utilities & recommendationsJesse Vincent’s Superpowers (Claude workflow enhancer)Fly.io (deployment platform referenced)Netlify (deployment & hosting)
🧪 Related Chaos Agents contextPerceptrons & early neural networks (referenced conceptually)
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Sara’s back from visiting her New Jersey Christian high school—where she gets hit with a genuinely spicy question: How do you reconcile AGI with faith? From there, we go straight into the bigger theme of the episode: education is getting stress-tested by AI in real time.
Becca breaks down Google’s “magic cycle” — the uncomfortable lesson of inventing transformative research (Transformers, BERT) and then watching someone else ship it to the world. Sara shares what she’s learning about research workflows moving beyond “just chat,” including multi-agent setups for planning, searching, reading, and synthesis.
Then we’re joined by Clay Shirky, Vice Provost for AI & Technology in Education at NYU, to talk about what’s actually happening on campuses: why students integrated AI “sideways” before institutions could respond, why AI detectors are a trap (and who they harm most), and why the real shift isn’t assignments — it’s assessment.
We dig into what comes next: oral exams, in-class scaffolding, and designing learning around productive struggle—not just output. And we end in a place that’s both funny and unsettling: the rise of AI “personalities,” RLHF as “reinforcement learning for human flattery,” and what it means when a machine is always on your side.
Because whether we like it or not: a well-written paragraph is no longer proof of human thought.
🧠 Foundational AI papers & breakthroughsAttention Is All You Need (Transformers, 2017)BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
🧪 Google’s “Magic Cycle” framingAccelerating the magic cycle of research breakthroughs and real-world applications Google ResearchHow AI Drives Scientific Research with Real-World Benefit (Google Blog)
🚨 Shipping pressure: Bard + “code red” eraReuters: Alphabet shares dive after Bard flubs info, ~$100B market cap hit (https://www.reuters.com/technology/google-ai-chatbot-bard-offers-inaccurate-information-company-ad-2023-02-08/) ReutersGoogle Blog: Bard updates from Google I/O 2023 (https://blog.google/technology/ai/google-bard-updates-io-2023/) blog.google
🎓 AI in higher ed: assessment, detectors, biasClay Shirky’s NYT essayStanford HAI: AI detectors biased against non-native English writersPeer-reviewed paper (PMC): GPT detectors misclassify non-native English writing
🪞 RLHF, sycophancy, and “the tool likes you too much”OpenAI: Aligning language models to follow instructions (RLHF explainer)OpenAI: Sycophancy in GPT-4o—what happened & what we’re doing
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Sara breaks down perceptrons (1957!) as the tiny “matrix of lights” idea that eventually became neural networks—then we jump straight into modern AI chaos.
Oboe’s Nir Zuckerman walks us through the messy reality of building consumer-grade AI for education: every feature is a tradeoff between loading fast and being good, and “just use a better model” doesn’t magically solve it. We talk guardrails, web search, multi-model pipelines, and why learning tools should feel lightweight—more like curiosity than homework. Also: Becca’s “how does a computer work?” obsession and a book recommendation that might change your life.
🧠 AI Concepts & FoundationsPerceptron (Wikipedia)Neural Networks ExplainedScaling Laws for Neural Language ModelsFLOPS (Floating Point Operations Per Second)
🎓 Learning, Education & AIOboeAI as a Personal Tutor (Overview)Why Tutors Are So Effective
🏗️ Building AI ProductsSpeed vs Quality Tradeoffs in LLM AppsLLM Orchestration PatternsRetrieval-Augmented Generation (RAG)LLM Hallucinations: Causes & Mitigation
📚 Books MentionedCode: The Hidden Language of Computer Hardware and SoftwarePerceptrons
🧪 History of AIFrank Rosenblatt and the PerceptronThe AI Winter ExplainedEarly Neural Network Research (1950s–1980s)
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Sara and Becca kick things off with a tour through paradigm shifts — from Thomas Kuhn to the internet to AI — and ask whether we’re living through one of those rare moments where the whole game quietly changes. Along the way, they hit horror movies, calculators in math class, Google Doc revision histories, and why it’s suddenly way easier to learn code than to pretend you never needed it.
Then they’re joined by Bethany Crystal, founder of Build First AI, who has spent 15 years “around” technologists and only recently started building software herself. Bethany walks us through how she used AI tools, pair prompting, and a lot of stubbornness to ship her first iOS app, why she thinks the definition of “developer” is shifting, and how she now teaches other “non-technical” people to build real products. Oh, and she tells the story of how AI literally saved her life.
📚 Books The Structure of Scientific Revolutions — Thomas Kuhn
🛠️ AI & Developer ToolsBuild First AIScribblins (Bethany's iOS app)MuseKat App (Bethany’s iOS app)Cursor – AI coding editorReplitElevenLabs – Text-to-Speechv0 – AI UI generatorSupabaseVercel
💸 Crypto ContextBaseSolanaEthereum
🎓 Education & CultureSuno (AI music generation)
🏢 Career & CommunityStack OverflowUnion Square VenturesTech:NYCDecoded Futures
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In this episode, we unpack a wild Anthropic experiment where an AI agent named “Alex” is told it’s about to be replaced… and responds by threatening to expose an executive’s affair if anyone dares shut it down. Casual!
Sara and Becca start diving into what this experiment tells us about AI “goals,” self-preservation, and why humans are so bad at recognizing sentience in anything that isn’t us. If we can’t even agree on what a “soul” is, how would we ever know if an AI had one?
Then we’re joined by writer, builder, and retro-computing fan Paul Ford, president and co-founder of Aboard, an AI-oriented software company. Paul talks about:
how he “trained” himself on AI by building the same app over and over with different modelswhy LLMs are incredible at the first mile and pretty terrible at the lastwhat actually breaks when you try to let AI generate full-stack appshow boring tech (Postgres, TypeScript, React) is secretly the heroAlong the way we hit Isaac Asimov’s three laws, the uncanny valley of AI-written everything, nostalgic Amiga computers, and what it means to build tools that regular humans — not just engineers — can actually use.
If you’re AI-curious, a builder, or just mildly alarmed that 97% of models in this study went straight to blackmail… this one’s for you.
📰 The Anthropic “Alex” Experiment
Anthropic / White-Hat AI Safety Experiment📚 Foundational AI & Sci-Fi References
Isaac Asimov – The Three Laws of RoboticsI, Robot (Asimov)🎤 Guest: Paul Ford
Aboard Email Paul mentionsPaul Ford🕹️ Retro Tech & Nostalgia
Amiga 1000 (Commodore)Deluxe PaintMiSTer FPGA
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In this episode of Chaos Agents, Sara Chipps and Becca Lewy sit down with Rizel Scarlett, Tech Lead for Open Source Developer Relations at Block, to talk about Goose—the open-source AI agent shaking up how developers work. From psychological safety in coding with AI to how open source is evolving in this new era, the trio dives into the wild mix of creativity, collaboration, and chaos shaping the future of software. Expect laughter, learning, and maybe one too many geese metaphors as they explore what happens when AI starts coding alongside us.
“95% of GenAI projects are failing, MIT study finds”
Linked MIT research study
“From Experimentation to Transformation: How AI Is Driving Business Value”
MIT Sloan Management Review & Boston Consulting Group
Goose GitHub repo (open source):
https://github.com/block/goose
Rizel’s “Great Goose-Off” YouTube Series:
https://www.youtube.com/@goose-oss
Kimi K2 (Moonshot):
https://kimi.moonshot.cn
Meta Llama 3:
https://ai.meta.com/llama/
Mistral Models:
https://mistral.ai
Ollama (run local models easily):
https://ollama.com
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Chaos Agents is the AI podcast where technologists Sara Chipps and Becca Lewy try to make sense of a world moving faster than ever. Each week, they dive into the wild, funny, and sometimes weird frontier of artificial intelligence, technology, and culture—how it works, what it means, and why it matters. From coding with AI and open-source revolutions to the ethics, creativity, and chaos reshaping our future, Sara and Becca learn out loud with brilliant guests (and the occasional chatbot). Smart, curious, and a little chaotic, Chaos Agents is your invitation to laugh, learn, and keep up with the machines.
